Existing methods for representing vehicles in a virtual environment typically involve computer simulations. In a common computer simulation, computers are used to simulate vehicle dynamics using algorithms. The accuracy of computer simulations heavily relies on how well the models are programmed, trained, and validated. Training and validation is often time-consuming and expensive, but necessary to generate high fidelity computer simulations. Even when the computer simulation is thoroughly validated, the computer simulation is still limited as a mathematical representation of reality and is inherently an approximation of the kinematics of the operation of the vehicles being simulated. Such approximations have a tendency to undesirably simplify many of the complexities of the actual system that is being represented. However, such simplifications may be necessary in conventional simulations as the significant processing loads can lengthen response times to unacceptable levels.
Another approach used in conventional simulations is to use historical data collected from a real environment to assist in the creation and training of a virtual environment. However, historical data may be difficult to obtain, may require interaction with environments which may not be available, and may not include enough flexibility to make ad hoc simulations. As a result, traditional methods of representing vehicles in a virtual environment may lack an acceptable level of dynamic accuracy and feasibility.